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Yussuff I.O. et. al./LAUTECH Journal of Engineering and Technology 13(1) 2019:38-44
38
INVESTIGATION INTO PATH LOSS PERFORMANCE FOR LONG TERM
EVOLUTION WITH KEY PERFORMANCE INDEX
Yussuff A. I. O. 1, Adenowo A. A. 1, Adeyeye A. H. 2
1Department of Electronic and Computer Engineering,Lagos State University, Nigeria
2Department of Electrical Engineering, The Ibarapa Polytechnic, Nigeria
Corresponding Author:AbayomiIsiaka O. Yussuff, Department of Electronic and Computer Engineering,
Lagos State University, Nigeria. Email: abayomi.yussuff@lasu.edu.ng.
ABSTRACT:
This work involved investigation into path loss (PL) performance evaluation for long term evolution (LTE)
with key performance index. PL model for prediction of cellular transmission, enhance spectral efficiency
relay and channel capacity for three terrains (hilly areas with thick vegetation, hilly terrains with
exceptional or uniform vegetation, and smooth terrains or rural area with low vegetation) were evaluated.
PL of 114dB and 159dB were obtained for downlink distances of 5km and 20km, respectively for Standford
University Interim (SUI) and Cost-231 Hata. The result shows increase in the throughput per user when
compared to the average throughput achieved in Cost-231. Again, uplink data traffic from communications
equipment towards the network core associated with higher spectral efficiency for uplink PL are 159 and
190dB, respectively for LTE systems. This is attributable to the relative distance between the transmitting
and the receiving stations. Hence, higher spectra efficiency utilization for LTE is not currently feasible for
Lagos and other major cities in Nigeria.
Keywords: Attenuation, Downlionk, Long Term Evolution, Path Loss, Throughput, Uplink.
INTRODUCTION
The wireless broadband technology is
expected to maintain handheld devices and cell
phones to support internet access(Ramkumar and
Gunasekaran, 2013). The volume of connected
devices is estimated to exceed the world
population by 2019. Therefore, it essential to make
provision for systems that will deliver such
projection. Operators are rolling out Long Term
Evolution (LTE) to meet the ever-increasing
request for sophisticated data rate. To aid spectral
efficiency, relay deployment is mobilized to propel
and increase the channel capability for the entire
network throughput(Hamid and Kostanic, 2013).
With high request for sophisticated mobile,
broadband channel is increasing rapidly. To meet
with the present and future demands for reliable
data rates over the mobile networks, the
International Telecommunication Union (ITU) set
up the International Mobile Telecommunication
(IMT)-Advanced, whose key feature is to provide
enhanced data rates for outdoors and indoors
purposes. IMT-Advanced standards offered
efficient modulation and access techniques
(Ahmed and Makki, 2010). The transmitting
power ratio of the receiving power is recognized as
Path Loss (PL). This refers to the highest
transmitting power a transmitter can transmit and
receive while recovering the original data (Liu et
al., 2017). PL is a significant factor in the energy
budget of each mobile cell. For this reason, it is
important to estimate or measure PL and its
performance with accuracy to provide effective
and stable connection in the entire region
(Atanasov and Kiss’ovski, 2013). 3GPP relay was
investigated in LTE networks for the uplink and
downlink average throughput for evaluation of
antenna model. This work appraises the current
LTE network deployment in Lagos, Nigeria. It
presents a synopsis of LTE deployment in Lagos
using key performance index data obtained from
Huawei Nigeria Limited, in order to realize faster
data throughputs on the current LTE system.
Various studies have been conducted on
PL performance for LTE deployment using
different approaches. First generation (1G) is an
analog cellular system with circuit switched
network architecture. It is suitable for simple
telephony but has low capacity and narrow
coverage area. Second generation (2G) wireless
technology is designed for the capacity
enhancement of increasing voice telephony,
limited data services and text messaging, and with
capability of permitting transmission of other
packets with small power range(Mayuri and
Manish, 2012). 2.5G is an extension of 2G and
offers greater capacity of Radio Frequency
channels and sophisticated throughput data
rates(Kanojia et al., 2012). Third generation (3G)
mobile and wireless technology surpasses 2G
technology and precedes the fourth-generation
(4G) technology. 4G is a widespread, high-speed
wireless service (IEEE LAN/MAN Standards
LAUTECH Journal of Engineering and Technology 13 (1) 2019: 38-44
Yussuff I.O. et. al./LAUTECH Journal of Engineering and Technology 13(1) 2019:38-44
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Committee, 2006). The feature and end user’s
expectations of 4G networks and the problem of
the new trends is discussed in IEEE LAN/MAN
Standards Committee(2006), Andrews et
al.(2007), Khan (2009) and Kale and
Jadhav(2013). 4G LTE gives quality wireless
mobile data rate up to 300Mbps, only second to
MIMO (Mawjoud, 2013).
PL channels at 2300MHz, 2600MHz and
3500 MHz was investigated and modeled by
Shabbir et al.(2011) using MATLAB software.
The predicted models used are Stanford University
Interim (SUI) model, COST (European
Cooperation in Science and Technology) 231
Walfisch-Ikegami model, ECC (Electronic
Communication Committee)-33/Hata Okumura
extended model and COST 231 Hata. Results from
various models revealed that minimum PL is
achieved with COST-231 Hata prediction model
(Sachin and Jadhav, 2013). It was found that the
optimum model value is very close to the measured
PL data for COST 231 Hata model, with a mean
error value lowered to a zero. The models
investigated by Shabbir et al. (2011)were
examined for city, sub city and remote areas.
Again, the findings also showed that COST 231
Hata model presented the least PL for diverse
frequencies. However, adaptability to distinct
areas by way of suitable modification for different
locations is the major advantage of this approach.
The result showed that PL is least in the city for the
frequencies between 1900 MHz and 2100 MHz
with SUI model while COST231 gave the greatest
PL. The computation of PL for different WiMAX
model at 3500 MHz via MATLAB in urban
settings was presented in Taruna and Kaur (2013).
The study adopted COST 231 Hata model, SUI
model, Ericsson model, ECC-33/Hata Okumura
comprehensive model and free space PL model,
and the best results were obtained via ECC-33 and
SUI models at different heights. In Tchao et al.
(2013), deterministic approach for bit error rate
(BER) was adopted for MIMO antenna
arrangement and several interferers in the Sub-
African settings. Despite of the interference
problem, poor estimation of average BER were
detected for MIMO placements. MIMO antenna
was configured efficiently with suppressed side
lobes to enhanced BER performance of WiMAX.
InCurwen and Whalley(2013), network
performances of different network sizes were
analyzed using MATLAB. The channel size was
simulated with different MIMO schemes against
probability error at different signal to noise ratio.
The result established that the maximum channel
size was attained at the highest decibel. In the case
of Gabriel (2017), 9.62Mbps downlink throughput
WiMAX network for realistic conditions in Sub-
Saharan African region was proposed. The
throughput performance was poor when compared
to the LTE networks deployment in other
continents. However, the results obtained from
some European and Asian countries were
presented in Kokane(2014) and Mollel and
Kisangiri (2014) respectively. In
Kokane(2014),the maximum reported measured
downlink throughput was 52Mbps whiles a
maximum throughput of 32Mbps is reported in
Mollel and Kisangiri (2014)
1. RESEARCH METHODOLOGY
Path loss models are useful wireless
cellular systems for planning that permits a set of
mathematical equations and procedures for radio
signal propagation in proposed strategic areas.
Basically, there are three types of models available
as shown in Figure 1. These are
i. Empirical Model,
ii. Stochastic Model, and
iii. Deterministic Model.
Figure 1: Block Diagram of Path Loss Propagation Model (Pathania et al., 2014).
The SUI model is an example of time
dispersive model while Cost 231 Hata model is
non-time dispersive model(Pathania et al., 2014).
In this work, Empirical Model (EM) was adopted.
2.1. Data Collection
Data was sourced from Huawei Nigeria
limited with key parameter index to predict PL
model for cellular transmission using Matlab.
These parameters are downlink cell traffic volume,
LTEdownlink average throughput, uplink data
traffic and LTE cell uplink average throughput. All
Yussuff I.O. et. al./LAUTECH Journal of Engineering and Technology 13(1) 2019:38-44
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the data were collected in Lagos from one of the
mobile network service provides at a frequency of
900/1800/2700 MHz.
2.2. Propagation Path Loss Models
These are generally referred to as the
reference formulas used in describing the
propagation loss encountered in the downlink
between transmitters (eNodeB). Lagos state has
over hundred thousand eNodebase stations and
receivers (Popoola et al., 2017). PL models are
used to achieve the objectives of this study.
2.2.1 Cost 231 Hata Model
This Model is broadly used for PL
prediction in wireless technology as an extension
of Hata-Okumura model. Hata models are
designed for frequency range between 500MHz to
2GHz for urban, suburban and rural environments
expansion. The frequency is out of range, it is
simple and has high access correction factors. This
Model call for the base station antenna to be higher
than all adjacent rooftop(Moges, 2016). The Cost
Hata model is premised on four prediction
propagation factors. These are frequency,
receiving antenna height, base station height and
distance between base station and the receivers’
antenna (Popoola et al., 2017). This Model is
limited to base station antenna heights greater than
30 meters and the PL can be expressed as follows
(AlAmmouri et al., 2017):
(1)
(2)
(3)
where A, B and C are factors that
depends on frequency and antenna height. C is
the environment correction factor, is the
carrier frequency of transmission (in MHz),
is he height of base station (in meters), and
is the hight of mobile station antenna (in
meters).The constant, is zero and 3 for
medium city, and metropolitan centers and
urban cities, respectively. The distance, is
approximately between 1 and 20 km. Carrier
Frequency ranges between 1.5 and 2 GHz, base
station heights are between 18 and 100 meters,
while mobile stationheight varies between
and10 meters.
2.2.2 SUI Model
The SUI is mutually designed by IEEE
802.16 group and Stanford University for
frequency below 11GHz. This model is an
extended Hata model for 1900 MHz and higher
frequency. Multipoint Microwave Distribution
System (MMDS) frequency band is between 2.5to
2.7GHz in some parts of the world. The height of
the base station antenna for SUI model is between
10 to 80 meters. The SUI model is applicable to
three types of terrains (A, B and C). Terrain A is
used in the hilly areas with thick vegetation. This
terrain has highest PL considering terrain a highly
populated urban area. Terrain B is good for hilly
terrains with exceptional vegetation, or uniform
vegetation. The intermediate PL scheme is
considered for suburban environment. Terrain C is
appropriate for smooth terrains or rural area with
low vegetation; PL is minimum for this terrain.
2. RESULTS AND DISCUSSION
As shown in the downlink cellular
network plots of Figures 2 and 3, which was
constructed from the data collected from Huawei
Nigeria Limited downlink data traffic given on
Table1, the Base Stations (BSs) are spatially
distributed according to homogeneous Poisson
point (Claesson and Edholm, 2016). It is evident
that the dataset is crowded by mobile users. The
time of day and the density of population
significantly affected the downlink speeds. The
downlink speeds are observed to be significantly
higher in urban areas when compared to some rural
regions. Table 1 shows Terrains A, B and C for
densely populated urban area, suburban
environment and flat or rural area respectively.
Table 1: Parameter for Different Terrains for SUI Model
Model Parameter TerrainA TerrainB Terrain C
a 4.6 4 3.6
b 0.0075 0.0065 0.005
c 12.6 17.1 20
Yussuff I.O. et. al./LAUTECH Journal of Engineering and Technology 13(1) 2019:38-44
41
Figure 2: PL for Down Link Data Traffic Figure 3: SUI for Down Link Data Traffic
Again, results for the predicted PL were
obtained at 159dB. This is the amount of signal
loss (i.e, attenuation) between a typical receiver
and transmitter. Path loss of 114dB and 159dB
were obtained for distances of 5km and 20km,
respectively. This is attributable to the relative
distance between the transmitting and the
receiving stations.From Figures 4 and 5, the factors
governing the efficiency of downlink throughput
are bandwidth, MIMO usage, modulation and
coding. The relative poor performance in this
regard could be associated to the relative far
distance between the transmitting antenna and the
receiving antenna.
Figure 4: PL for Average Throughput Figure 5: SUI for Average Throughput
Figures 6 and 7 showed that uplink data
traffic (from a ground station up to a satellite)
from data communications equipment towards the
network core is associated with higher spectral
efficiency. This higher spectral efficiency is not
achievable in Lagos due to higher PL obtained
values of 159 and 190dB, respectively, especially
for LTE network operated at 800 and 1800MHz.
100 200 300 400 500 600 700 800 900
158
158.2
158.4
158.6
158.8
159
159.2
159.4
159.6
159.8
160
(PL)Predicted Pathloss(dB)
Down link Data Tra ffic(MB)
Predicted Pathloss Versus the (DL) Data Traffic
100 200 300 400 500 600 700 800 900
189
189.2
189.4
189.6
189.8
190
190.2
190.4
190.6
190.8
191
(SUI)Predicted Pathloss(dB)
DownLink Data Traffic(MB)
Predicted (SUI) Pathloss Versus the Down Link Data Traffic
3500 4000 4500 5000 5500 6000
158
158.2
158.4
158.6
158.8
159
159.2
159.4
159.6
159.8
160
(PL) Predicted Pathloss(dB)
DL Average Throughput (Kbps)
Predicted Pathloss Versus the DL Avera ge Throughput
3500 4000 4500 5000 5500 6000
189
189.2
189.4
189.6
189.8
190
190.2
190.4
190.6
190.8
191
(SUI)Predicted Pathloss(dB)
DL Average Throughput (Kbps)
Predicted (SUI) Pathloss Versus the DL Avera ge Throughput
Yussuff I.O. et. al./LAUTECH Journal of Engineering and Technology 13(1) 2019:38-44
42
Figure 6: PL for Uplink Data Traffic Figure 7: SUI for Uplink Data Traffic
Furthermore, comparing the PL parameters obtained with respect to the uplink average throughput
data obtained at 159 and 190dB as shown in Figures 8 and 9, it is appropriate to conclude that spectral efficiency
is underutilized.
Figure 8: PL for Uplink Average Throughput Figure 9: SUI for Uplink Average Throughput
The throughput of a user equipment in a
physical segment at distance from the base station
or receiver station, given a number of transmitting
and receiving antennas, the cell center users are
users who do not need relay station, and
transmitting data directly to eNodeB with higher
data rate than when using receiving station.
The correction factors and mathematical model is
formulated and presented by Popoola et al. (2017)
as:
(4)
PL is measured in decibel (dB),
represents the distance between the transmitter and
the receiver, is the reference distance (taken as
100m), is the frequency modification factor,
is the modification factor for base station height,
represents shadowing and represents the PL
component. Furthermore, d is the space between
BS and mobile antenna, in meters, λ is the
wavelength in meters, log is the distributed factor
, for the propagations path fading, with values
ranging between 8.2 and 10.6 dB(AlAmmouri et
al., 2017). Comparing this model with other
empirical models, the model in Equation (4) is
found to be suitable for PL predictions. To
determine fading scale and the receiver signal
strength, a value between 1 to 20 km is suitable for
mobile channel characterization for technologies
like GSM (Global System for Mobile
20 40 60 80 100 120 140 160 180 200
158
158.2
158.4
158.6
158.8
159
159.2
159.4
159.6
159.8
160
(PL)Predicted Pathloss(dB)
UpLink Data Traffic (MB)
Predicted Pathloss Versus the UpLink Data Traffic
20 40 60 80 100 120 140 160 180 200
189
189.2
189.4
189.6
189.8
190
190.2
190.4
190.6
190.8
191
(SUI)Predicted Pathloss(dB)
UpLink Data Traffic (MB)
Predicted (SUI) Pathloss Versus the UpLink Data Traffic
700 800 900 1000 1100 1200 1300 1400
158
158.2
158.4
158.6
158.8
159
159.2
159.4
159.6
159.8
160
(PL)Predicted Pathloss(dB)
UpLink Average Throughput (Kbps)
Predicted Pathloss Versus The Up Link Average Throughput
700 800 900 1000 1100 1200 1300 1400
189
189.2
189.4
189.6
189.8
190
190.2
190.4
190.6
190.8
191
(SUI)Predicted Pathloss(dB)
UpLink Average Throughput (Kbps)
Predicted (SUI) Pathloss Versus The Up Link Average Throughput
Yussuff I.O. et. al./LAUTECH Journal of Engineering and Technology 13(1) 2019:38-44
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Communications). From Pathania et al. (2014),
(5)
Also, the PL exponent γ is given by
(6)
where, the parameter is the base station antenna
height, in meters. The height is between 10 and 80
meters. The parameters a, b, and c are determined by the
terrain category, the parameter for free space
propagation, the frequency modification factor and
the modification for receiver antenna height for the
model are expressed by AlAmmouri et al.(2017) as:
(7)
for terrain
types A and B (8)
where, is the operating frequency in MHz, and receiver
antenna height is (m). These correction factors
facilitate extended the applicability of this model in the
prediction of PL for all three types of terrains in rural,
urban and suburban environments.
3. CONCLUSION
PL performance evaluation for LTE
systems were investigated using key performance
index. Notwithstanding the deployment of the LTE
systems in major cities of Nigeria, slow internet
speed information download has continued to be a
pointer to the perennial challenge confronting
Internet users in Nigeria.
Furthermore, PL for SUI model was evaluated
using the average throughput per user in a narrower
but higher range of 0.75-1.8 Mbps. The result
shows increase in the throughput per user when
compared to the average throughput achieved in
Cost-231. For this, radio access technology was
observed to present lower to the throughput of
wide area network. Again, for a typical node,
throughput approached a ratio between the
capacities of low coverage and high throughput
radio access technologies to the high coverage.
Hence, Cost -231 Hata model PL prediction was
obtained at 159dB at 5km while SUI model gave a
predicted DL PL of 190dB, translating to a
maximum attenuation of 114dB between receiver
and transmitter, for same 5km distance. As a result,
higher spectra efficiency utilization for LTE is not
currently feasible for Lagos and other major cities
in Nigeria. To achieve this, relevant optimization
techniques are required to manage the tradeoffs
between power level, frequency reconfiguration
and other spectrum efficiency. There is urgent need
for Nigerian Communication Commission
(NCC)to design appropriate pricing model that
would encourage mobile network operators and
Internet Service Providers. However, subscribers
should be ready and willing to pay more for
improved reliability, efficiency and faster data
rates.
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